Extreme learning machines: a survey

  • Guang-Bin HuangEmail author
  • Dian Hui Wang
  • Yuan Lan
Original Article


Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational scalability. Extreme learning machine (ELM) as emergent technology which overcomes some challenges faced by other techniques has recently attracted the attention from more and more researchers. ELM works for generalized single-hidden layer feedforward networks (SLFNs). The essence of ELM is that the hidden layer of SLFNs need not be tuned. Compared with those traditional computational intelligence techniques, ELM provides better generalization performance at a much faster learning speed and with least human intervene. This paper gives a survey on ELM and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELM, (3) online sequential ELM, (4) incremental ELM, and (5) ensemble of ELM.


Extreme learning machine Support vector machine ELM kernel ELM feature space Ensemble Incremental learning Online sequential learning 



This research was sponsored by the grant from Academic Research Fund (AcRF) Tier 1 under project no. RG 22/08 (M52040128).


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Department of Computer Science and Computer EngineeringLa Trobe UniversityMelbourneAustralia

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